Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Fei Cheng, Masayuki Asahara, Ichiro Kobayashi, Sadao Kurohashi · 2020
Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time and document creation time (DCT).It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information.2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) 1 hinder from using the whole data.This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs.Our model deals with three TLINK categories with multi-task learning to leverage the full size of data.The experimental results show that our proposal outperforms state-of-the-art models and two transfer learning baselines on both the English and Japanese data.